Neural network prediction of thermal field spatiotemporal evolution during additive manufacturing: an overview
This paper provides an overview of the application of machine learning (ML) techniques for predicting the spatiotemporal evolution of thermal fields during additive manufacturing (AM) processes. AM, also known as three-dimensional printing, has gained significant attention in various industries due...
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| Published in | International journal of advanced manufacturing technology Vol. 134; no. 5-6; pp. 2107 - 2128 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Springer London
01.09.2024
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| Abstract | This paper provides an overview of the application of machine learning (ML) techniques for predicting the spatiotemporal evolution of thermal fields during additive manufacturing (AM) processes. AM, also known as three-dimensional printing, has gained significant attention in various industries due to its potential for rapid prototyping and customized production. However, accurately predicting and controlling the thermal behavior during the AM process is crucial for ensuring the quality and reliability of the printed components. Traditional physics-based models (PBM) often face challenges in capturing AM’s complex dynamics and inherent uncertainties. In recent years, ML algorithms, particularly neural networks (NNs), have shown promising results in predicting the thermal field evolution. This paper reviews the existing literature and highlights the critical methodologies and recent advancements in NN-based predictions. It explores novel perspectives by discussing the hybrid modeling approaches, including the combination of PBMs with NNs. This overview highlights the evolving landscape of predictive techniques in the context of AM and underscores the potential for enhancing accuracy and efficiency in thermal field prediction. The paper also discusses the challenges and outlines future directions for enhancing the accuracy and efficiency of thermal field prediction in AM. By synthesizing current research, this overview will guide researchers and practitioners toward leveraging NNs effectively for optimizing thermal management in AM processes. The insights presented underscore the transformative potential of NN predictions in advancing AM capabilities. |
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| AbstractList | This paper provides an overview of the application of machine learning (ML) techniques for predicting the spatiotemporal evolution of thermal fields during additive manufacturing (AM) processes. AM, also known as three-dimensional printing, has gained significant attention in various industries due to its potential for rapid prototyping and customized production. However, accurately predicting and controlling the thermal behavior during the AM process is crucial for ensuring the quality and reliability of the printed components. Traditional physics-based models (PBM) often face challenges in capturing AM’s complex dynamics and inherent uncertainties. In recent years, ML algorithms, particularly neural networks (NNs), have shown promising results in predicting the thermal field evolution. This paper reviews the existing literature and highlights the critical methodologies and recent advancements in NN-based predictions. It explores novel perspectives by discussing the hybrid modeling approaches, including the combination of PBMs with NNs. This overview highlights the evolving landscape of predictive techniques in the context of AM and underscores the potential for enhancing accuracy and efficiency in thermal field prediction. The paper also discusses the challenges and outlines future directions for enhancing the accuracy and efficiency of thermal field prediction in AM. By synthesizing current research, this overview will guide researchers and practitioners toward leveraging NNs effectively for optimizing thermal management in AM processes. The insights presented underscore the transformative potential of NN predictions in advancing AM capabilities. |
| Author | Faiz Wan Ali, Wan Fahmin Chike, Onuchukwu Godwin Ahmad, Norhayati |
| Author_xml | – sequence: 1 givenname: Onuchukwu Godwin orcidid: 0000-0002-9041-7496 surname: Chike fullname: Chike, Onuchukwu Godwin email: onuchukwuchike@graduate.utm.my organization: Faculty of Mechanical Engineering, UTM, Faculty of Engineering, Nigerian Army University Biu, NAUB – sequence: 2 givenname: Norhayati surname: Ahmad fullname: Ahmad, Norhayati organization: Faculty of Mechanical Engineering, UTM – sequence: 3 givenname: Wan Fahmin surname: Faiz Wan Ali fullname: Faiz Wan Ali, Wan Fahmin email: wan_fahmin@utm.my organization: Faculty of Mechanical Engineering, UTM |
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| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Thermal field Deep neural network Additive manufacturing Spatiotemporal evolution Physics-based modeling Machine learning |
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